Over the past several decades, computers have transformed work in almost every sector of the economy (1). Today, we are at the beginning of an even larger and more rapid transformation due to recent advances in machine learning. Machine learning based on neural networks is arguably the branch of artificial intelligence that will have the most widespread near-term commercial impact, because of its ability to accelerate the pace of automation itself.

Like the steam engine and electricity, MACHINE LEARNING is a general-purpose technology, capable of spawning a plethora of additional innovations and capabilities (2). However, there is not yet a widely-shared agreement on the tasks where MACHINE LEARNING systems will excel. Therefore, there is little agreement on the specific expected effects on the workforce and on the economy more broadly.

Recently, Erik Brynjolfsson of MIT and and Tom Mitchell of Carnegie Mellon addressed these questions in a study published in the journal Science. That study, based upon an understanding of what the current generation of MACHINE LEARNING systems can and cannot do, examined the key implications of MACHINE LEARNING for the workforce.

It found that while parts of many jobs may be “suitable for MACHINE LEARNING,” other tasks within these same jobs do not fit the criteria for MACHINE LEARNING well. Hence, its effects on employment are more complex than the simple “replacement and substitution story” emphasized by most analysts. Furthermore, the current economic effects of MACHINE LEARNING are relatively limited; so, we are not facing the imminent “end of work,” which is sometimes proclaimed. However, the longer-term implications for the economy and the workforce are profound.

One of the biggest areas of concern about machine learning is its impact on economic inequality. To date, automation is just one factor, others include increased globalization and mass migration. However, the potential for large and rapid changes due to MACHINE LEARNING, suggests that its economic effects could be disruptive, creating both winners and losers. Ensuring a positive outcome will require considerable attention form policy-makers, business leaders, technologists, and researchers.

Attention to unintended consequences is important because the implications of MACHINE LEARNING are harder to forecast than those of previous digital technologies...